Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade.  The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning. Best JavaScript Machine Learning Libraries JavaScript is a well organized, lightweight, and easy to understand and use programming language. Although most of its uses are as a scripting language for web development, there are also non-browser environments that make use of JavaScript. If you’ve been working with JavaScript and are curious to get involved in the JavaScript Machine Learning scene, we have just the thing for you. In this section, we will cover some of the top JavaScript libraries to get you started with Machine Learning. Read on! 1. Brain.js Website: brain.js.org GitHub Link: github.com/BrainJS/brain.js Brain.js is an extremely fast and easy-to-use JavaScript library for neural networks. The library makes use of a GPU for all its processing needs by converting them into GPU- compatible shaders, but when a GPU isn’t available, brain.js elegantly falls back to executing in regular JavaScript. Using the train() of the brain.js library, you can train your NN models. The framework that made it on top of the list of the best Javascript Machine Learning Libraries offers support for a plethora of Neural Networks, such as Feedforward NN, Recurrent NN, Long Short Term Memory NN, and a few more to provide the best of their features. As for the integrations, brain.js supports importing and exporting models via JSON or as a function. 2. stdlib Website: stdlib.io GitHub Link: github.com/stdlib/lib Short for Standard Lib, stdlib is a standard library for JavaScript and Node.js that offers a range of numerical and scientific calculation capabilities for your Machine Learning applications. As one of the top frameworks on the list of the best Javascript Machine Learning Libraries the library comes with a wide array of mathematical functions to help you build statistical models, data visualization capabilities to transform your data into visuals and extract insights, while also including other helpful utilities that make application development and library development easier. Stdlib’s integrated read-eval-print-loop makes use of the numerous built-in functions and helps in manipulating data, enabling quick prototyping. The stdlib library also serves as a great all-in-one general purpose package that comes packed with extensive documentation, including several high-quality examples, tests, and benchmarks to help you start developing for Machine Learning. 3. TensorFlow.js Website: tensorflow.org/js GitHub Link: github.com/tensorflow/tfjs Google’s Tensorflow.js is one of the most popular Machine Learning and Deep Learning libraries for JavaScript. With tensorflow.js, you can perform a range of tasks, such as building and training Machine Learning models, running JavaScript and non-JavaScript Machine Learning models on the browser or Node.js, and even retraining existing models. It can also process pre-trained models as long as they’re from Keras or a TensorFlow SavedModel format. All that tensorflow.js has to offer is broken into simple and efficient APIs, such as the core API, layers API, converter API, and more. The library also comes with built-in support for different backgrounds or platforms, such as WebGL, WebGPU, CPU backend, Node, to name a few. To give you an idea of its popularity, tensorflow.js is already being used in diverse sectors like education and healthcare. 4. Deeplearnjs GitHub Link: github.com/cghawthorne/deeplearnjs Now turned into tensorflow.js core, Deeplearnjs is an intuitive open-source JavaScript Machine Learning library on the list of the best Javascript Machine Learning Libraries. The deeplearnjs library supports hardware-acceleration and comes equipped with all the building blocks required for training and running pre-trained Machine Learning models in your browser. The library offers two APIs, one is an immediate execution model like NumPy, while the other is a deferred execution model resembling the TensorFlow API. The deeplearnjs library was originally developed by the Google Brain’s PAIR team for the development of deep learning models and has been used for a variety of projects such as education, art, understanding models. 5. Machinelearn.js Website: machinelearnjs.com GitHub Link: github.com/machinelearnjs/machinelearnjs Written in TypeScript, Machinelearn.js is a ScikitLearn like JavaScript Machine Learning library for the web and Node.js. It shares similarity with brain.js as both of these libraries emphasize on making Machine Learning as simple and straightforward as possible. The library is perfect for solving Machine Learning problems and teaching the workings of the several Machine Learning algorithms available with its learning APIs. At its core, machinelearn.js uses TensorFlow.js to provide an all-in-one library for ML developers with features, such as clustering, bagging, ensemble, linear models, feature extraction. Although the library is quite fast, you can enable acceleration using C++ binding or by using a GPU. 6. Mind Website: stevenmiller888.github.io/mindjs.net/ GitHub Link: github.com/stevenmiller888/mind The Mind library for JavaScript lets you easily train Neural Networks in your browser or in Node.js to make better predictions. Mind as one of the best Javascript Machine Learning Libraries is pretty flexible to work with and offers features such as a matrix implementation for processing training data, network topology customizability, and the ability to download or upload minds that have completed learning. Mind’s website is home to an interesting implementation of the library in the form of a movie recommendation system that provides a hands-on experience and showcases its capabilities. 7. ConvNetJS Website: cs.stanford.edu/people/karpathy/convnetjs/ GitHub Link: github.com/karpathy/convnetjs The beauty of JavaScript libraries is that some of them come with zero software requirements, you just need to have a browser, and that’s it. The same can be said for ConvNetJS, which is a JavaScript Machine Learning library for training your Deep Learning models entirely from your browser. Apart from supporting browsers, the library also supports Node.js. At the moment, ConvNetJS offers support for classification, regression (L2) cost functions, training Convolutional Networks for image processing, common Neural Network modules containing non-linearities and fully connected layers, and an experimental Reinforcement Learning module based-off Deep Q Learning. The library has some browser-based demos to give you an idea about it. But do keep in mind that the developer is not actively maintaining the library anymore. 8. Neuro.js Website: neuro.js.org GitHub Link: github.com/janhuenermann/neurojs Neuro.js is a JavaScript Machine Learning library built with a primary reason for creating chatbots and smart Artificial Intelligence-powered assistants by the likes of Google Assistant, Siri, and so on. The library is also good for creating and training JavaScript Machine Learning models and deploying them in the browser or Node.js. The simple and performance-driven Neuro.js library aims to make Machine Learning practical and accessible to everyone granting it its well-deserved spot on the list of the best javascript machine learning libraries. This open-source library comes with several features, such as online learning, real-time classification, supports binary as well as multi-label classification, and more. 9. WebDNN Website: mil-tokyo.github.io/webdnn GitHub Link: github.com/mil-tokyo/webdnn WebDNN is a Deep Neural Network framework developed by the University of Tokyo’s Machine Intelligence Laboratory to solve the growing computational requirements and to increase the availability of a DNN for end-users. The WebDNN’s JavaScript API makes it easy to run DNNs on the browsers by optimizing the trained DNN models by compressing them, which greatly helps with the accelerated execution of the said models. The user of these efficient JavaScript APIs, such as WebAssembly and WebGPU is the key to achieving practically zero execution overhead with WebDNN. 10. Keras.js Website: transcranial.github.io/keras-js GitHub Link: github.com/transcranial/keras-js Keras.js is another fairly popular and open-source JavaScript library that lets you run Keras’ Machine Learning models in your browser. Keras, one of the best Javascript Machine Learning Libraries, also includes GPU support via WebGL. You can run the models in Node.js, but they’ll be restricted to CPU mode only, which means no GPU acceleration. As Keras abstracts several frameworks as backend, you can train your models on any backend of your choice, including CNTK and TensorFlow. The demos folder in their GitHub repo contains over 8 interactive demos based on real-world problems that can give you a hands-on idea of the capabilities and working of keras-js. 11. Synaptic Website: caza.la/synapti GitHub Link: github.com/cazala/synaptic Synaptic is a JavaScript library for Neural Networks, which you can use from browser and Node.js. Claimed to be an architecture-free library, synaptic lets you build and train any kind of first or second-order Neural Network architectures. The library comes with a handful of built-in architectures, including multilayer perceptrons, liquid state machines, multilayer long-short term memory networks, and a trainer that can train any network. You can check out some of the implementations of synaptic, such as read from Wikipedia, self-organizing map, learn image filters, and more from the demos available on its GitHub page. 12. Compromise Website: compromise.cool GitHub Link: github.com/spencermountain/compromise In the words of its creator, compromise is a modest Natural Language Processing library on the list of the best Javascript Machine Learning Libraries that is both fast and lightweight. The goal behind the compromise library is to have a tool that can interpret and pre-parse the text quickly while you’re typing it but be accurate enough to provide relevant results. The API provided by the compromise library includes a variety of useful functions, such as constructors, utilities, accessors, match, tags, loops, and much more for parsing and manipulating text. Apart from the functions, compromise also comes with a range of extensions that include adjectives functions, date functions, numbers functions among others. We suggest you take a look at the GitHub page to get the full picture. More JavaScript Machine Learning Libraries Find a list of other frameworks below that didn`t make it on the list of the best javascript machine learning libraries Neataptic DeepForge FlappyLearning LimduJS PropelJS Conclusion So far, we went over 12 of the notable JavaScript libraries in the list of best javascript machine learning libraries that you can use for your Machine Learning tasks. These libraries are perfect for training your ML models in the browser or Node.js, thanks to their lightweight nature. Considering the performance complications with JavaScript, it obviously may not be a very popular option for Machine Learning, but it is on the path to becoming one. But if you are looking for a Python alternative for Machine Learning, why not take a look at JavaScript and its collection of ML-oriented libraries. We hope you learned something from this write-up. Don’t forget to share your thoughts on the best Javascript Machine Learning Libraries if you made it this far. Previously published at https://liquidary.com/top-12-best-javascript-machine-learning-libraries/ About Contributor Claire is a Content Crafter and Marketer at who specializes in technology and businesses. He is passionate about blogging and helps his clients to achieve online success. Digitalogy Autowhale or Liquidary may have a (business) relationship with the submitting party. Image Source: Unsplash Disclaimer. Liquidary and Autowhale do not endorse articles submitted through this form. Solely the submitting party is responsible and accountable for the accuracy and compliance of each article. Liquidary has the right to delete or hide articles any time without further notice.

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